Overview

Dataset statistics

Number of variables20
Number of observations129647
Missing cells185379
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 MiB
Average record size in memory160.0 B

Variable types

Numeric6
Categorical5
Text6
Unsupported1
DateTime2

Alerts

Fiscal_Year has constant value ""Constant
CheckVoidDt has constant value ""Constant
ObjectId is highly overall correlated with Budget_Type and 1 other fieldsHigh correlation
InvoiceID is highly overall correlated with CheckIDHigh correlation
InvoiceAmt is highly overall correlated with DistributionAmt and 1 other fieldsHigh correlation
DistributionAmt is highly overall correlated with InvoiceAmtHigh correlation
CheckID is highly overall correlated with InvoiceIDHigh correlation
CheckAmt is highly overall correlated with InvoiceAmtHigh correlation
Budget_Type is highly overall correlated with ObjectId and 1 other fieldsHigh correlation
Agency_Name is highly overall correlated with ObjectId and 1 other fieldsHigh correlation
Budget_Type is highly imbalanced (62.3%)Imbalance
Category is highly imbalanced (61.1%)Imbalance
DepartmentName has 10013 (7.7%) missing valuesMissing
Sub_DepartmentName has 45703 (35.3%) missing valuesMissing
Stimulus_Type has 129647 (100.0%) missing valuesMissing
InvoiceAmt is highly skewed (γ1 = 47.65706828)Skewed
DistributionAmt is highly skewed (γ1 = 110.28375)Skewed
CheckAmt is highly skewed (γ1 = 20.69431244)Skewed
ObjectId is uniformly distributedUniform
ObjectId has unique valuesUnique
Stimulus_Type is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-31 13:38:21.150927
Analysis finished2023-12-31 13:38:38.993595
Duration17.84 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ObjectId
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct129647
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64824
Minimum1
Maximum129647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:39.163200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6483.3
Q132412.5
median64824
Q397235.5
95-th percentile123164.7
Maximum129647
Range129646
Interquartile range (IQR)64823

Descriptive statistics

Standard deviation37426.01
Coefficient of variation (CV)0.57734804
Kurtosis-1.2
Mean64824
Median Absolute Deviation (MAD)32412
Skewness0
Sum8.4042371 × 109
Variance1.4007062 × 109
MonotonicityStrictly increasing
2023-12-31T19:08:39.495582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
86427 1
 
< 0.1%
86440 1
 
< 0.1%
86439 1
 
< 0.1%
86438 1
 
< 0.1%
86437 1
 
< 0.1%
86436 1
 
< 0.1%
86435 1
 
< 0.1%
86434 1
 
< 0.1%
86433 1
 
< 0.1%
Other values (129637) 129637
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
129647 1
< 0.1%
129646 1
< 0.1%
129645 1
< 0.1%
129644 1
< 0.1%
129643 1
< 0.1%
129642 1
< 0.1%
129641 1
< 0.1%
129640 1
< 0.1%
129639 1
< 0.1%
129638 1
< 0.1%

Fiscal_Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
2008
129647 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters518588
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2008
3rd row2008
4th row2008
5th row2008

Common Values

ValueCountFrequency (%)
2008 129647
100.0%

Length

2023-12-31T19:08:39.786217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:08:39.995621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2008 129647
100.0%

Most occurring characters

ValueCountFrequency (%)
0 259294
50.0%
2 129647
25.0%
8 129647
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 518588
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 259294
50.0%
2 129647
25.0%
8 129647
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 518588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 259294
50.0%
2 129647
25.0%
8 129647
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 518588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 259294
50.0%
2 129647
25.0%
8 129647
25.0%

Budget_Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
Metro Government Operations
120167 
Metro Government Capital
 
9480

Length

Max length27
Median length27
Mean length26.780635
Min length24

Characters and Unicode

Total characters3472029
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetro Government Capital
2nd rowMetro Government Capital
3rd rowMetro Government Capital
4th rowMetro Government Capital
5th rowMetro Government Capital

Common Values

ValueCountFrequency (%)
Metro Government Operations 120167
92.7%
Metro Government Capital 9480
 
7.3%

Length

2023-12-31T19:08:40.227377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:08:40.439010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
metro 129647
33.3%
government 129647
33.3%
operations 120167
30.9%
capital 9480
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 509108
14.7%
t 388941
11.2%
n 379461
10.9%
r 379461
10.9%
o 379461
10.9%
259294
 
7.5%
a 139127
 
4.0%
p 129647
 
3.7%
i 129647
 
3.7%
M 129647
 
3.7%
Other values (7) 648235
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2823794
81.3%
Uppercase Letter 388941
 
11.2%
Space Separator 259294
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 509108
18.0%
t 388941
13.8%
n 379461
13.4%
r 379461
13.4%
o 379461
13.4%
a 139127
 
4.9%
p 129647
 
4.6%
i 129647
 
4.6%
m 129647
 
4.6%
v 129647
 
4.6%
Other values (2) 129647
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
M 129647
33.3%
G 129647
33.3%
O 120167
30.9%
C 9480
 
2.4%
Space Separator
ValueCountFrequency (%)
259294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3212735
92.5%
Common 259294
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 509108
15.8%
t 388941
12.1%
n 379461
11.8%
r 379461
11.8%
o 379461
11.8%
a 139127
 
4.3%
p 129647
 
4.0%
i 129647
 
4.0%
M 129647
 
4.0%
m 129647
 
4.0%
Other values (6) 518588
16.1%
Common
ValueCountFrequency (%)
259294
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3472029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 509108
14.7%
t 388941
11.2%
n 379461
10.9%
r 379461
10.9%
o 379461
10.9%
259294
 
7.5%
a 139127
 
4.0%
p 129647
 
3.7%
i 129647
 
3.7%
M 129647
 
3.7%
Other values (7) 648235
18.7%

Agency_Name
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
Related Agencies
25738 
Public Works & Assets Department
21290 
Public Protection Department
15976 
Parks & Recreation
14046 
Housing & Family Services
12589 
Other values (25)
40008 

Length

Max length37
Median length29
Mean length22.128372
Min length7

Characters and Unicode

Total characters2868877
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWaterfront Development Corp
2nd rowWaterfront Development Corp
3rd rowWaterfront Development Corp
4th rowWaterfront Development Corp
5th rowWaterfront Development Corp

Common Values

ValueCountFrequency (%)
Related Agencies 25738
19.9%
Public Works & Assets Department 21290
16.4%
Public Protection Department 15976
12.3%
Parks & Recreation 14046
10.8%
Housing & Family Services 12589
9.7%
Elected Officials 8397
 
6.5%
Metro Police 8117
 
6.3%
Public Health & Wellness 7401
 
5.7%
Economic Development 3564
 
2.7%
Neighborhoods Department 2700
 
2.1%
Other values (20) 9829
 
7.6%

Length

2023-12-31T19:08:40.708419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58273
14.5%
public 44667
 
11.1%
department 43429
 
10.8%
related 25738
 
6.4%
agencies 25738
 
6.4%
works 21290
 
5.3%
assets 21290
 
5.3%
protection 15976
 
4.0%
parks 14046
 
3.5%
recreation 14046
 
3.5%
Other values (46) 116440
29.0%

Most occurring characters

ValueCountFrequency (%)
e 368393
 
12.8%
271286
 
9.5%
t 214331
 
7.5%
s 185564
 
6.5%
i 177886
 
6.2%
c 153553
 
5.4%
r 140648
 
4.9%
l 140218
 
4.9%
n 136550
 
4.8%
a 132807
 
4.6%
Other values (35) 947641
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2196350
76.6%
Uppercase Letter 342297
 
11.9%
Space Separator 271286
 
9.5%
Other Punctuation 58944
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 368393
16.8%
t 214331
9.8%
s 185564
8.4%
i 177886
8.1%
c 153553
 
7.0%
r 140648
 
6.4%
l 140218
 
6.4%
n 136550
 
6.2%
a 132807
 
6.0%
o 130318
 
5.9%
Other values (11) 416082
18.9%
Uppercase Letter
ValueCountFrequency (%)
P 82976
24.2%
D 47221
13.8%
A 47178
13.8%
R 43199
12.6%
W 28905
 
8.4%
H 21470
 
6.3%
S 14270
 
4.2%
F 12724
 
3.7%
E 12138
 
3.5%
M 12029
 
3.5%
Other values (10) 20187
 
5.9%
Other Punctuation
ValueCountFrequency (%)
& 58063
98.5%
' 533
 
0.9%
/ 348
 
0.6%
Space Separator
ValueCountFrequency (%)
271286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2538647
88.5%
Common 330230
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 368393
14.5%
t 214331
 
8.4%
s 185564
 
7.3%
i 177886
 
7.0%
c 153553
 
6.0%
r 140648
 
5.5%
l 140218
 
5.5%
n 136550
 
5.4%
a 132807
 
5.2%
o 130318
 
5.1%
Other values (31) 758379
29.9%
Common
ValueCountFrequency (%)
271286
82.2%
& 58063
 
17.6%
' 533
 
0.2%
/ 348
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2868877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 368393
 
12.8%
271286
 
9.5%
t 214331
 
7.5%
s 185564
 
6.5%
i 177886
 
6.2%
c 153553
 
5.4%
r 140648
 
4.9%
l 140218
 
4.9%
n 136550
 
4.8%
a 132807
 
4.6%
Other values (35) 947641
33.0%
Distinct580
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:41.121982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length81
Median length66
Mean length21.244178
Min length3

Characters and Unicode

Total characters2754244
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)0.1%

Sample

1st rowBelle Capital Improvements
2nd rowBelle Capital Improvements
3rd rowBelle Capital Improvements
4th rowBelle Capital Improvements
5th rowBelle Capital Improvements
ValueCountFrequency (%)
31531
 
9.1%
services 16453
 
4.7%
division 14589
 
4.2%
facilities 13338
 
3.8%
fleet 13321
 
3.8%
louisville 13040
 
3.8%
library 10272
 
3.0%
metro 8380
 
2.4%
zoo 8153
 
2.3%
operations 7528
 
2.2%
Other values (1012) 210835
60.7%
2023-12-31T19:08:41.931907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 276398
 
10.0%
e 273408
 
9.9%
217793
 
7.9%
o 185323
 
6.7%
t 183723
 
6.7%
n 169949
 
6.2%
r 168732
 
6.1%
a 147685
 
5.4%
s 147394
 
5.4%
l 112343
 
4.1%
Other values (62) 871496
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2128120
77.3%
Uppercase Letter 344852
 
12.5%
Space Separator 217793
 
7.9%
Other Punctuation 40829
 
1.5%
Decimal Number 17424
 
0.6%
Dash Punctuation 5104
 
0.2%
Open Punctuation 61
 
< 0.1%
Close Punctuation 61
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 276398
13.0%
e 273408
12.8%
o 185323
8.7%
t 183723
8.6%
n 169949
8.0%
r 168732
7.9%
a 147685
 
6.9%
s 147394
 
6.9%
l 112343
 
5.3%
c 89526
 
4.2%
Other values (16) 373639
17.6%
Uppercase Letter
ValueCountFrequency (%)
F 42345
12.3%
S 35332
10.2%
D 33570
9.7%
P 28725
 
8.3%
M 27034
 
7.8%
L 25420
 
7.4%
C 19183
 
5.6%
A 18892
 
5.5%
E 16846
 
4.9%
O 16099
 
4.7%
Other values (16) 81406
23.6%
Decimal Number
ValueCountFrequency (%)
0 5834
33.5%
1 4254
24.4%
2 3110
17.8%
8 2367
13.6%
4 715
 
4.1%
6 391
 
2.2%
7 354
 
2.0%
5 321
 
1.8%
9 48
 
0.3%
3 30
 
0.2%
Other Punctuation
ValueCountFrequency (%)
& 31445
77.0%
/ 3763
 
9.2%
, 3166
 
7.8%
' 1822
 
4.5%
. 623
 
1.5%
? 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
217793
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 61
100.0%
Close Punctuation
ValueCountFrequency (%)
) 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2472972
89.8%
Common 281272
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 276398
 
11.2%
e 273408
 
11.1%
o 185323
 
7.5%
t 183723
 
7.4%
n 169949
 
6.9%
r 168732
 
6.8%
a 147685
 
6.0%
s 147394
 
6.0%
l 112343
 
4.5%
c 89526
 
3.6%
Other values (42) 718491
29.1%
Common
ValueCountFrequency (%)
217793
77.4%
& 31445
 
11.2%
0 5834
 
2.1%
- 5104
 
1.8%
1 4254
 
1.5%
/ 3763
 
1.3%
, 3166
 
1.1%
2 3110
 
1.1%
8 2367
 
0.8%
' 1822
 
0.6%
Other values (10) 2614
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2754244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 276398
 
10.0%
e 273408
 
9.9%
217793
 
7.9%
o 185323
 
6.7%
t 183723
 
6.7%
n 169949
 
6.2%
r 168732
 
6.1%
a 147685
 
5.4%
s 147394
 
5.4%
l 112343
 
4.1%
Other values (62) 871496
31.6%

DepartmentName
Text

MISSING 

Distinct251
Distinct (%)0.2%
Missing10013
Missing (%)7.7%
Memory size1013.0 KiB
2023-12-31T19:08:42.332154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length35
Mean length17.826772
Min length3

Characters and Unicode

Total characters2132688
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowMetro Council Operations
2nd rowMetro Council Operations
3rd rowMetro Council Operations
4th rowMetro Council Operations
5th rowMetro Council Operations
ValueCountFrequency (%)
services 20784
 
7.6%
13186
 
4.8%
administration 9077
 
3.3%
facilities 8895
 
3.2%
management 8840
 
3.2%
finance 8056
 
2.9%
maintenance 7283
 
2.7%
collection 6654
 
2.4%
fleet 5934
 
2.2%
of 5799
 
2.1%
Other values (319) 180223
65.6%
2023-12-31T19:08:43.065430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 216847
 
10.2%
i 206035
 
9.7%
n 185302
 
8.7%
155097
 
7.3%
t 138946
 
6.5%
a 132516
 
6.2%
o 114461
 
5.4%
r 113779
 
5.3%
s 109314
 
5.1%
c 96252
 
4.5%
Other values (58) 664139
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1678982
78.7%
Uppercase Letter 269562
 
12.6%
Space Separator 155097
 
7.3%
Other Punctuation 18835
 
0.9%
Decimal Number 7992
 
0.4%
Open Punctuation 1095
 
0.1%
Close Punctuation 1095
 
0.1%
Dash Punctuation 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 216847
12.9%
i 206035
12.3%
n 185302
11.0%
t 138946
8.3%
a 132516
7.9%
o 114461
6.8%
r 113779
 
6.8%
s 109314
 
6.5%
c 96252
 
5.7%
l 79875
 
4.8%
Other values (16) 285655
17.0%
Uppercase Letter
ValueCountFrequency (%)
F 32838
12.2%
S 32814
12.2%
A 28766
10.7%
C 23207
 
8.6%
P 19303
 
7.2%
M 18998
 
7.0%
O 14987
 
5.6%
B 14333
 
5.3%
R 12609
 
4.7%
D 11674
 
4.3%
Other values (14) 60033
22.3%
Decimal Number
ValueCountFrequency (%)
1 3474
43.5%
9 1182
 
14.8%
2 1003
 
12.6%
0 910
 
11.4%
3 533
 
6.7%
5 301
 
3.8%
4 212
 
2.7%
6 210
 
2.6%
8 90
 
1.1%
7 77
 
1.0%
Other Punctuation
ValueCountFrequency (%)
& 13186
70.0%
' 2704
 
14.4%
/ 2294
 
12.2%
, 651
 
3.5%
Space Separator
ValueCountFrequency (%)
155097
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1095
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1095
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1948544
91.4%
Common 184144
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 216847
 
11.1%
i 206035
 
10.6%
n 185302
 
9.5%
t 138946
 
7.1%
a 132516
 
6.8%
o 114461
 
5.9%
r 113779
 
5.8%
s 109314
 
5.6%
c 96252
 
4.9%
l 79875
 
4.1%
Other values (40) 555217
28.5%
Common
ValueCountFrequency (%)
155097
84.2%
& 13186
 
7.2%
1 3474
 
1.9%
' 2704
 
1.5%
/ 2294
 
1.2%
9 1182
 
0.6%
( 1095
 
0.6%
) 1095
 
0.6%
2 1003
 
0.5%
0 910
 
0.5%
Other values (8) 2104
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2132688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 216847
 
10.2%
i 206035
 
9.7%
n 185302
 
8.7%
155097
 
7.3%
t 138946
 
6.5%
a 132516
 
6.2%
o 114461
 
5.4%
r 113779
 
5.3%
s 109314
 
5.1%
c 96252
 
4.5%
Other values (58) 664139
31.1%

Sub_DepartmentName
Text

MISSING 

Distinct394
Distinct (%)0.5%
Missing45703
Missing (%)35.3%
Memory size1013.0 KiB
2023-12-31T19:08:43.463506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length17.905878
Min length4

Characters and Unicode

Total characters1503091
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowDOH Administration
2nd rowDOH Administration
3rd rowDOH Administration
4th rowDOH Administration
5th rowDOH Administration
ValueCountFrequency (%)
services 16867
 
9.2%
management 12208
 
6.6%
11123
 
6.0%
facilities 8891
 
4.8%
maintenance 6669
 
3.6%
operations 6112
 
3.3%
fleet 5934
 
3.2%
administration 4705
 
2.6%
library 3429
 
1.9%
materials 3266
 
1.8%
Other values (554) 105113
57.0%
2023-12-31T19:08:44.200026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 172740
 
11.5%
i 142579
 
9.5%
n 127650
 
8.5%
a 114062
 
7.6%
t 106232
 
7.1%
100373
 
6.7%
r 93154
 
6.2%
s 74456
 
5.0%
o 63938
 
4.3%
c 60468
 
4.0%
Other values (56) 447439
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1197265
79.7%
Uppercase Letter 190049
 
12.6%
Space Separator 100373
 
6.7%
Other Punctuation 14673
 
1.0%
Dash Punctuation 384
 
< 0.1%
Open Punctuation 126
 
< 0.1%
Close Punctuation 115
 
< 0.1%
Decimal Number 106
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 172740
14.4%
i 142579
11.9%
n 127650
10.7%
a 114062
9.5%
t 106232
8.9%
r 93154
7.8%
s 74456
 
6.2%
o 63938
 
5.3%
c 60468
 
5.1%
l 52801
 
4.4%
Other values (16) 189185
15.8%
Uppercase Letter
ValueCountFrequency (%)
S 31874
16.8%
M 26330
13.9%
F 20076
10.6%
A 15285
8.0%
P 14306
7.5%
C 12787
 
6.7%
O 10788
 
5.7%
D 9198
 
4.8%
B 7934
 
4.2%
I 7308
 
3.8%
Other values (15) 34163
18.0%
Decimal Number
ValueCountFrequency (%)
0 45
42.5%
7 36
34.0%
3 15
 
14.2%
4 5
 
4.7%
1 4
 
3.8%
6 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
& 10673
72.7%
/ 2757
 
18.8%
' 735
 
5.0%
. 256
 
1.7%
, 252
 
1.7%
Space Separator
ValueCountFrequency (%)
100373
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 384
100.0%
Open Punctuation
ValueCountFrequency (%)
( 126
100.0%
Close Punctuation
ValueCountFrequency (%)
) 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1387314
92.3%
Common 115777
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 172740
12.5%
i 142579
 
10.3%
n 127650
 
9.2%
a 114062
 
8.2%
t 106232
 
7.7%
r 93154
 
6.7%
s 74456
 
5.4%
o 63938
 
4.6%
c 60468
 
4.4%
l 52801
 
3.8%
Other values (41) 379234
27.3%
Common
ValueCountFrequency (%)
100373
86.7%
& 10673
 
9.2%
/ 2757
 
2.4%
' 735
 
0.6%
- 384
 
0.3%
. 256
 
0.2%
, 252
 
0.2%
( 126
 
0.1%
) 115
 
0.1%
0 45
 
< 0.1%
Other values (5) 61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1503091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 172740
 
11.5%
i 142579
 
9.5%
n 127650
 
8.5%
a 114062
 
7.6%
t 106232
 
7.1%
100373
 
6.7%
r 93154
 
6.2%
s 74456
 
5.0%
o 63938
 
4.3%
c 60468
 
4.0%
Other values (56) 447439
29.8%

Category
Categorical

IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
Contractual Services
76627 
Supplies
49363 
Equipment/Capital Outlay
 
1729
Interdepartment Charges
 
1431
Other Expenses
 
295
Other values (3)
 
202

Length

Max length24
Median length20
Mean length15.499047
Min length8

Characters and Unicode

Total characters2009405
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowContractual Services
2nd rowContractual Services
3rd rowContractual Services
4th rowContractual Services
5th rowContractual Services

Common Values

ValueCountFrequency (%)
Contractual Services 76627
59.1%
Supplies 49363
38.1%
Equipment/Capital Outlay 1729
 
1.3%
Interdepartment Charges 1431
 
1.1%
Other Expenses 295
 
0.2%
Personal Services 199
 
0.2%
Restricted 2
 
< 0.1%
Interagency Charges 1
 
< 0.1%

Length

2023-12-31T19:08:44.510590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:08:44.795398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
services 76826
36.6%
contractual 76627
36.5%
supplies 49363
23.5%
equipment/capital 1729
 
0.8%
outlay 1729
 
0.8%
charges 1432
 
0.7%
interdepartment 1431
 
0.7%
other 295
 
0.1%
expenses 295
 
0.1%
personal 199
 
0.1%
Other values (2) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 211559
10.5%
t 163034
 
8.1%
a 161504
 
8.0%
r 158244
 
7.9%
c 153456
 
7.6%
i 129649
 
6.5%
l 129647
 
6.5%
u 129448
 
6.4%
s 128412
 
6.4%
S 126189
 
6.3%
Other values (19) 518263
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1715736
85.4%
Uppercase Letter 211658
 
10.5%
Space Separator 80282
 
4.0%
Other Punctuation 1729
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 211559
12.3%
t 163034
9.5%
a 161504
9.4%
r 158244
9.2%
c 153456
8.9%
i 129649
7.6%
l 129647
7.6%
u 129448
7.5%
s 128412
7.5%
p 103910
6.1%
Other values (10) 246873
14.4%
Uppercase Letter
ValueCountFrequency (%)
S 126189
59.6%
C 79788
37.7%
E 2024
 
1.0%
O 2024
 
1.0%
I 1432
 
0.7%
P 199
 
0.1%
R 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
80282
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1729
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1927394
95.9%
Common 82011
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 211559
11.0%
t 163034
 
8.5%
a 161504
 
8.4%
r 158244
 
8.2%
c 153456
 
8.0%
i 129649
 
6.7%
l 129647
 
6.7%
u 129448
 
6.7%
s 128412
 
6.7%
S 126189
 
6.5%
Other values (17) 436252
22.6%
Common
ValueCountFrequency (%)
80282
97.9%
/ 1729
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2009405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 211559
10.5%
t 163034
 
8.1%
a 161504
 
8.0%
r 158244
 
7.9%
c 153456
 
7.6%
i 129649
 
6.5%
l 129647
 
6.5%
u 129448
 
6.4%
s 128412
 
6.4%
S 126189
 
6.3%
Other values (19) 518263
25.8%
Distinct315
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:45.203037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length37
Mean length22.821808
Min length4

Characters and Unicode

Total characters2958779
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowPayments to Contractors
2nd rowPayments to Contractors
3rd rowPayments to Contractors
4th rowPayments to Contractors
5th rowPayments to Contractors
ValueCountFrequency (%)
services 38688
 
11.2%
supplies 33848
 
9.8%
grant 10974
 
3.2%
office 10321
 
3.0%
9691
 
2.8%
travel 9521
 
2.8%
community 8412
 
2.4%
payments 7853
 
2.3%
automotive 7789
 
2.3%
equipment 6975
 
2.0%
Other values (400) 200964
58.2%
2023-12-31T19:08:45.936111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 303232
 
10.2%
i 246091
 
8.3%
215389
 
7.3%
s 191725
 
6.5%
n 183711
 
6.2%
t 166618
 
5.6%
r 165075
 
5.6%
a 157124
 
5.3%
o 155660
 
5.3%
l 134884
 
4.6%
Other values (54) 1039270
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2349791
79.4%
Uppercase Letter 351449
 
11.9%
Space Separator 215389
 
7.3%
Other Punctuation 39736
 
1.3%
Decimal Number 1340
 
< 0.1%
Dash Punctuation 384
 
< 0.1%
Open Punctuation 345
 
< 0.1%
Close Punctuation 345
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 303232
12.9%
i 246091
10.5%
s 191725
 
8.2%
n 183711
 
7.8%
t 166618
 
7.1%
r 165075
 
7.0%
a 157124
 
6.7%
o 155660
 
6.6%
l 134884
 
5.7%
c 122918
 
5.2%
Other values (16) 522753
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 78540
22.3%
C 37641
10.7%
P 29086
 
8.3%
A 26198
 
7.5%
T 24255
 
6.9%
E 24231
 
6.9%
R 18869
 
5.4%
O 18362
 
5.2%
M 16887
 
4.8%
G 13558
 
3.9%
Other values (11) 63822
18.2%
Decimal Number
ValueCountFrequency (%)
2 790
59.0%
0 175
 
13.1%
1 144
 
10.7%
4 60
 
4.5%
3 44
 
3.3%
8 28
 
2.1%
9 26
 
1.9%
7 25
 
1.9%
5 24
 
1.8%
6 24
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 24624
62.0%
& 9691
 
24.4%
, 5421
 
13.6%
Space Separator
ValueCountFrequency (%)
215389
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 384
100.0%
Open Punctuation
ValueCountFrequency (%)
( 345
100.0%
Close Punctuation
ValueCountFrequency (%)
) 345
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2701240
91.3%
Common 257539
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 303232
 
11.2%
i 246091
 
9.1%
s 191725
 
7.1%
n 183711
 
6.8%
t 166618
 
6.2%
r 165075
 
6.1%
a 157124
 
5.8%
o 155660
 
5.8%
l 134884
 
5.0%
c 122918
 
4.6%
Other values (37) 874202
32.4%
Common
ValueCountFrequency (%)
215389
83.6%
/ 24624
 
9.6%
& 9691
 
3.8%
, 5421
 
2.1%
2 790
 
0.3%
- 384
 
0.1%
( 345
 
0.1%
) 345
 
0.1%
0 175
 
0.1%
1 144
 
0.1%
Other values (7) 231
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2958779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 303232
 
10.2%
i 246091
 
8.3%
215389
 
7.3%
s 191725
 
6.5%
n 183711
 
6.2%
t 166618
 
5.6%
r 165075
 
5.6%
a 157124
 
5.3%
o 155660
 
5.3%
l 134884
 
4.6%
Other values (54) 1039270
35.1%

Stimulus_Type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing129647
Missing (%)100.0%
Memory size1013.0 KiB
Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:46.374075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length12
Mean length13.83887
Min length3

Characters and Unicode

Total characters1794168
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowCapital Project Fund
2nd rowCapital Project Fund
3rd rowCapital Project Fund
4th rowCapital Project Fund
5th rowCapital Project Fund
ValueCountFrequency (%)
fund 105809
36.0%
general 101610
34.6%
federally 6333
 
2.2%
funded 6333
 
2.2%
thru 5565
 
1.9%
federal 5408
 
1.8%
pass 5369
 
1.8%
other 5276
 
1.8%
2007 3259
 
1.1%
year 2857
 
1.0%
Other values (145) 45983
15.7%
2023-12-31T19:08:47.123917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 259484
14.5%
n 231733
12.9%
164155
9.1%
r 137707
7.7%
a 137142
7.6%
d 136527
7.6%
l 128949
7.2%
F 126711
7.1%
u 125558
7.0%
G 105025
5.9%
Other values (56) 241177
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1296377
72.3%
Uppercase Letter 305851
 
17.0%
Space Separator 164155
 
9.1%
Decimal Number 26998
 
1.5%
Connector Punctuation 561
 
< 0.1%
Other Punctuation 119
 
< 0.1%
Open Punctuation 53
 
< 0.1%
Close Punctuation 53
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 259484
20.0%
n 231733
17.9%
r 137707
10.6%
a 137142
10.6%
d 136527
10.5%
l 128949
9.9%
u 125558
9.7%
t 25910
 
2.0%
i 19705
 
1.5%
s 18503
 
1.4%
Other values (14) 75159
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
F 126711
41.4%
G 105025
34.3%
P 9988
 
3.3%
C 9736
 
3.2%
O 8916
 
2.9%
A 7939
 
2.6%
T 5661
 
1.9%
H 4612
 
1.5%
S 4503
 
1.5%
Y 3750
 
1.2%
Other values (13) 19010
 
6.2%
Decimal Number
ValueCountFrequency (%)
0 12318
45.6%
2 6737
25.0%
7 3326
 
12.3%
4 818
 
3.0%
3 771
 
2.9%
5 767
 
2.8%
6 742
 
2.7%
1 644
 
2.4%
8 541
 
2.0%
9 334
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 89
74.8%
' 24
 
20.2%
& 5
 
4.2%
. 1
 
0.8%
Space Separator
ValueCountFrequency (%)
164155
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 561
100.0%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 53
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1602228
89.3%
Common 191940
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 259484
16.2%
n 231733
14.5%
r 137707
8.6%
a 137142
8.6%
d 136527
8.5%
l 128949
8.0%
F 126711
7.9%
u 125558
7.8%
G 105025
6.6%
t 25910
 
1.6%
Other values (37) 187482
11.7%
Common
ValueCountFrequency (%)
164155
85.5%
0 12318
 
6.4%
2 6737
 
3.5%
7 3326
 
1.7%
4 818
 
0.4%
3 771
 
0.4%
5 767
 
0.4%
6 742
 
0.4%
1 644
 
0.3%
_ 561
 
0.3%
Other values (9) 1101
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1794168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 259484
14.5%
n 231733
12.9%
164155
9.1%
r 137707
7.7%
a 137142
7.6%
d 136527
7.6%
l 128949
7.2%
F 126711
7.1%
u 125558
7.0%
G 105025
5.9%
Other values (56) 241177
13.4%
Distinct10689
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:47.576599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length62
Mean length21.116216
Min length2

Characters and Unicode

Total characters2737654
Distinct characters50
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3981 ?
Unique (%)3.1%

Sample

1st rowBERGER INC
2nd rowCED INC
3rd rowDINE COMPANY
4th rowDIVERSIFIED SHEET METAL INC
5th rowMARINE INDUSTRIES CORPORATION
ValueCountFrequency (%)
inc 52349
 
12.0%
14548
 
3.3%
co 7522
 
1.7%
of 6207
 
1.4%
group 6125
 
1.4%
officemax 6073
 
1.4%
aramark 5795
 
1.3%
company 5608
 
1.3%
uniform 5317
 
1.2%
career 5298
 
1.2%
Other values (9545) 319964
73.6%
2023-12-31T19:08:48.405823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
305159
 
11.1%
E 240996
 
8.8%
A 215908
 
7.9%
R 201106
 
7.3%
I 200840
 
7.3%
N 195502
 
7.1%
C 171116
 
6.3%
O 157356
 
5.7%
S 143952
 
5.3%
L 135465
 
4.9%
Other values (40) 770254
28.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2407963
88.0%
Space Separator 305159
 
11.1%
Other Punctuation 21255
 
0.8%
Decimal Number 1721
 
0.1%
Dash Punctuation 1120
 
< 0.1%
Close Punctuation 216
 
< 0.1%
Open Punctuation 216
 
< 0.1%
Connector Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 240996
 
10.0%
A 215908
 
9.0%
R 201106
 
8.4%
I 200840
 
8.3%
N 195502
 
8.1%
C 171116
 
7.1%
O 157356
 
6.5%
S 143952
 
6.0%
L 135465
 
5.6%
T 130516
 
5.4%
Other values (16) 615206
25.5%
Decimal Number
ValueCountFrequency (%)
4 305
17.7%
2 290
16.9%
0 279
16.2%
1 247
14.4%
3 176
10.2%
9 139
8.1%
7 89
 
5.2%
5 68
 
4.0%
8 65
 
3.8%
6 63
 
3.7%
Other Punctuation
ValueCountFrequency (%)
& 19342
91.0%
. 838
 
3.9%
/ 441
 
2.1%
' 320
 
1.5%
# 310
 
1.5%
* 3
 
< 0.1%
! 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
305159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1120
100.0%
Close Punctuation
ValueCountFrequency (%)
) 216
100.0%
Open Punctuation
ValueCountFrequency (%)
( 216
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2407963
88.0%
Common 329691
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 240996
 
10.0%
A 215908
 
9.0%
R 201106
 
8.4%
I 200840
 
8.3%
N 195502
 
8.1%
C 171116
 
7.1%
O 157356
 
6.5%
S 143952
 
6.0%
L 135465
 
5.6%
T 130516
 
5.4%
Other values (16) 615206
25.5%
Common
ValueCountFrequency (%)
305159
92.6%
& 19342
 
5.9%
- 1120
 
0.3%
. 838
 
0.3%
/ 441
 
0.1%
' 320
 
0.1%
# 310
 
0.1%
4 305
 
0.1%
2 290
 
0.1%
0 279
 
0.1%
Other values (14) 1287
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2737654
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
305159
 
11.1%
E 240996
 
8.8%
A 215908
 
7.9%
R 201106
 
7.3%
I 200840
 
7.3%
N 195502
 
7.1%
C 171116
 
6.3%
O 157356
 
5.7%
S 143952
 
5.3%
L 135465
 
4.9%
Other values (40) 770254
28.1%

InvoiceID
Real number (ℝ)

HIGH CORRELATION 

Distinct111589
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637760.8
Minimum563638
Maximum708943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:48.716311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum563638
5-th percentile579427.3
Q1605478.5
median637444
Q3670010.5
95-th percentile696517.4
Maximum708943
Range145305
Interquartile range (IQR)64532

Descriptive statistics

Standard deviation37506.141
Coefficient of variation (CV)0.058809103
Kurtosis-1.1756835
Mean637760.8
Median Absolute Deviation (MAD)32264
Skewness0.012801063
Sum8.2683774 × 1010
Variance1.4067106 × 109
MonotonicityNot monotonic
2023-12-31T19:08:49.008220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
673400 76
 
0.1%
638608 76
 
0.1%
608539 69
 
0.1%
622870 65
 
0.1%
641713 65
 
0.1%
651907 65
 
0.1%
663047 65
 
0.1%
594748 62
 
< 0.1%
597775 61
 
< 0.1%
685077 60
 
< 0.1%
Other values (111579) 128983
99.5%
ValueCountFrequency (%)
563638 1
< 0.1%
567776 1
< 0.1%
567780 1
< 0.1%
567781 1
< 0.1%
567782 1
< 0.1%
567783 1
< 0.1%
567784 1
< 0.1%
567785 1
< 0.1%
567786 1
< 0.1%
567787 1
< 0.1%
ValueCountFrequency (%)
708943 1
 
< 0.1%
707170 1
 
< 0.1%
707071 1
 
< 0.1%
706799 1
 
< 0.1%
706798 1
 
< 0.1%
706759 1
 
< 0.1%
706651 1
 
< 0.1%
706649 3
< 0.1%
706646 1
 
< 0.1%
706644 1
 
< 0.1%
Distinct781
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
Minimum2003-01-07 05:00:00+00:00
Maximum2008-07-18 03:59:59+00:00
2023-12-31T19:08:49.335382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:49.646012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

InvoiceAmt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct38055
Distinct (%)29.4%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3750.9798
Minimum-56235.36
Maximum5000000
Zeros0
Zeros (%)0.0%
Negative1095
Negative (%)0.8%
Memory size1013.0 KiB
2023-12-31T19:08:49.985661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-56235.36
5-th percentile10
Q150
median164.47
Q3662.5
95-th percentile9250
Maximum5000000
Range5056235.4
Interquartile range (IQR)612.5

Descriptive statistics

Standard deviation32691.635
Coefficient of variation (CV)8.7154921
Kurtosis5150.0186
Mean3750.9798
Median Absolute Deviation (MAD)144.47
Skewness47.657068
Sum4.8627327 × 108
Variance1.068743 × 109
MonotonicityNot monotonic
2023-12-31T19:08:50.307813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 2760
 
2.1%
130 1300
 
1.0%
25 1156
 
0.9%
50 1018
 
0.8%
15 905
 
0.7%
100 856
 
0.7%
20 811
 
0.6%
40 690
 
0.5%
14.2 655
 
0.5%
150 641
 
0.5%
Other values (38045) 118847
91.7%
ValueCountFrequency (%)
-56235.36 1
< 0.1%
-28512.14 1
< 0.1%
-11101.2 1
< 0.1%
-9158.66 1
< 0.1%
-6874.26 1
< 0.1%
-6508.16 1
< 0.1%
-5148 1
< 0.1%
-4203.59 1
< 0.1%
-4120.5 1
< 0.1%
-3747.91 1
< 0.1%
ValueCountFrequency (%)
5000000 1
 
< 0.1%
2538043 2
< 0.1%
2035700 1
 
< 0.1%
1596051.52 1
 
< 0.1%
1381943 1
 
< 0.1%
1378305 2
< 0.1%
1140037 2
< 0.1%
1091653.32 2
< 0.1%
1043127 1
 
< 0.1%
947187.01 3
< 0.1%

DistributionAmt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct42543
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1887.847
Minimum-131156.92
Maximum5000000
Zeros5
Zeros (%)< 0.1%
Negative1704
Negative (%)1.3%
Memory size1013.0 KiB
2023-12-31T19:08:50.615037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-131156.92
5-th percentile5.023
Q136
median125
Q3476.955
95-th percentile4684
Maximum5000000
Range5131156.9
Interquartile range (IQR)440.955

Descriptive statistics

Standard deviation22126.235
Coefficient of variation (CV)11.720354
Kurtosis21360.233
Mean1887.847
Median Absolute Deviation (MAD)110
Skewness110.28375
Sum2.447537 × 108
Variance4.8957028 × 108
MonotonicityNot monotonic
2023-12-31T19:08:50.934400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 2856
 
2.2%
130 1293
 
1.0%
25 1187
 
0.9%
15 1069
 
0.8%
50 1044
 
0.8%
100 912
 
0.7%
20 860
 
0.7%
40 708
 
0.5%
150 681
 
0.5%
35 676
 
0.5%
Other values (42533) 118361
91.3%
ValueCountFrequency (%)
-131156.92 1
< 0.1%
-110468.7 1
< 0.1%
-109947.76 1
< 0.1%
-81774.08 1
< 0.1%
-56235.36 1
< 0.1%
-50575 1
< 0.1%
-44867.5 1
< 0.1%
-42327.45 1
< 0.1%
-33455.39 1
< 0.1%
-31125.34 1
< 0.1%
ValueCountFrequency (%)
5000000 1
< 0.1%
2035700 1
< 0.1%
1596051.52 1
< 0.1%
1422811 1
< 0.1%
1381943 1
< 0.1%
1115232 1
< 0.1%
1100000 1
< 0.1%
1043127 1
< 0.1%
1000000 1
< 0.1%
779362.38 1
< 0.1%

CheckID
Real number (ℝ)

HIGH CORRELATION 

Distinct54641
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327410.33
Minimum291142
Maximum445951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:51.240158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum291142
5-th percentile297929
Q1310301.5
median327597
Q3343413.5
95-th percentile357892
Maximum445951
Range154809
Interquartile range (IQR)33112

Descriptive statistics

Standard deviation19187.618
Coefficient of variation (CV)0.058604191
Kurtosis-1.0990116
Mean327410.33
Median Absolute Deviation (MAD)16522
Skewness0.043037533
Sum4.2447767 × 1010
Variance3.6816467 × 108
MonotonicityNot monotonic
2023-12-31T19:08:51.555771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
326327 347
 
0.3%
306051 247
 
0.2%
306423 228
 
0.2%
302896 194
 
0.1%
323123 150
 
0.1%
351080 139
 
0.1%
324545 136
 
0.1%
307984 133
 
0.1%
346579 133
 
0.1%
354223 131
 
0.1%
Other values (54631) 127809
98.6%
ValueCountFrequency (%)
291142 2
< 0.1%
291145 1
< 0.1%
291146 1
< 0.1%
291155 1
< 0.1%
291159 1
< 0.1%
291163 1
< 0.1%
291167 1
< 0.1%
291177 1
< 0.1%
291181 1
< 0.1%
291183 1
< 0.1%
ValueCountFrequency (%)
445951 1
< 0.1%
435745 1
< 0.1%
430959 2
< 0.1%
426021 2
< 0.1%
422260 1
< 0.1%
420189 1
< 0.1%
415041 1
< 0.1%
409644 1
< 0.1%
407728 1
< 0.1%
405618 1
< 0.1%
Distinct305
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
Minimum2007-07-03 03:59:59+00:00
Maximum2009-09-14 03:59:59+00:00
2023-12-31T19:08:51.868350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:52.199331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CheckAmt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct24799
Distinct (%)19.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10333.631
Minimum0.4
Maximum5000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1013.0 KiB
2023-12-31T19:08:52.533475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile28.75
Q1211.22
median1000
Q33925
95-th percentile41707.58
Maximum5000000
Range4999999.6
Interquartile range (IQR)3713.78

Descriptive statistics

Standard deviation53537.916
Coefficient of variation (CV)5.1809395
Kurtosis901.37365
Mean10333.631
Median Absolute Deviation (MAD)908.48
Skewness20.694312
Sum1.3396416 × 109
Variance2.8663085 × 109
MonotonicityNot monotonic
2023-12-31T19:08:52.863330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 2488
 
1.9%
130 1200
 
0.9%
15 801
 
0.6%
100 577
 
0.4%
10 479
 
0.4%
200 467
 
0.4%
50 440
 
0.3%
140 433
 
0.3%
30896.02 347
 
0.3%
150 331
 
0.3%
Other values (24789) 122076
94.2%
ValueCountFrequency (%)
0.4 2
 
< 0.1%
0.8 6
< 0.1%
0.84 1
 
< 0.1%
0.86 1
 
< 0.1%
1 1
 
< 0.1%
1.1 1
 
< 0.1%
1.2 10
< 0.1%
1.25 1
 
< 0.1%
1.28 1
 
< 0.1%
1.35 1
 
< 0.1%
ValueCountFrequency (%)
5000000 1
 
< 0.1%
2538043 2
 
< 0.1%
2035700 1
 
< 0.1%
1596051.52 1
 
< 0.1%
1381943 1
 
< 0.1%
1378305 2
 
< 0.1%
1158251 56
< 0.1%
1140037 2
 
< 0.1%
1130982.01 6
 
< 0.1%
1111240 54
< 0.1%

CheckVoidDt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1013.0 KiB
1900/01/01 05:00:00+00
129647 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2852234
Distinct characters8
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1900/01/01 05:00:00+00
2nd row1900/01/01 05:00:00+00
3rd row1900/01/01 05:00:00+00
4th row1900/01/01 05:00:00+00
5th row1900/01/01 05:00:00+00

Common Values

ValueCountFrequency (%)
1900/01/01 05:00:00+00 129647
100.0%

Length

2023-12-31T19:08:53.154174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:08:53.373746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1900/01/01 129647
50.0%
05:00:00+00 129647
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1426117
50.0%
1 388941
 
13.6%
/ 259294
 
9.1%
: 259294
 
9.1%
9 129647
 
4.5%
129647
 
4.5%
5 129647
 
4.5%
+ 129647
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2074352
72.7%
Other Punctuation 518588
 
18.2%
Space Separator 129647
 
4.5%
Math Symbol 129647
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1426117
68.8%
1 388941
 
18.8%
9 129647
 
6.2%
5 129647
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 259294
50.0%
: 259294
50.0%
Space Separator
ValueCountFrequency (%)
129647
100.0%
Math Symbol
ValueCountFrequency (%)
+ 129647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2852234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1426117
50.0%
1 388941
 
13.6%
/ 259294
 
9.1%
: 259294
 
9.1%
9 129647
 
4.5%
129647
 
4.5%
5 129647
 
4.5%
+ 129647
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2852234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1426117
50.0%
1 388941
 
13.6%
/ 259294
 
9.1%
: 259294
 
9.1%
9 129647
 
4.5%
129647
 
4.5%
5 129647
 
4.5%
+ 129647
 
4.5%

Interactions

2023-12-31T19:08:35.300330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:28.108905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:29.611662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:31.084264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:32.518070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:33.875079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:35.549408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:28.344300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:29.857617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:31.323830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:32.736313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:34.112914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:35.764676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:28.585181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:30.104767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:31.555743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:32.973349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:34.368615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:35.908053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:28.809227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:30.345314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:31.788302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:33.199632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:34.596663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:36.057598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:29.050611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:30.577828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:32.071531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:33.424315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:34.825519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:36.292084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:29.373273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:30.846271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:32.302172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:33.659691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:08:35.070056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-31T19:08:53.519363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ObjectIdInvoiceIDInvoiceAmtDistributionAmtCheckIDCheckAmtBudget_TypeAgency_NameCategory
ObjectId1.000-0.019-0.039-0.086-0.0110.0730.8430.5350.183
InvoiceID-0.0191.0000.0050.0200.998-0.0070.0730.1070.041
InvoiceAmt-0.0390.0051.0000.8730.0050.5770.0360.0850.017
DistributionAmt-0.0860.0200.8731.0000.0180.4890.0320.0750.015
CheckID-0.0110.9980.0050.0181.000-0.0040.0510.0630.035
CheckAmt0.073-0.0070.5770.489-0.0041.0000.1220.0830.101
Budget_Type0.8430.0730.0360.0320.0510.1221.0000.5900.271
Agency_Name0.5350.1070.0850.0750.0630.0830.5901.0000.381
Category0.1830.0410.0170.0150.0350.1010.2710.3811.000

Missing values

2023-12-31T19:08:36.928862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-31T19:08:37.755378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-31T19:08:38.556673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ObjectIdFiscal_YearBudget_TypeAgency_NameSub_Agency_NameDepartmentNameSub_DepartmentNameCategorySub_CategoryStimulus_TypeFunding_SourceVendor_NameInvoiceIDInvoiceDtInvoiceAmtDistributionAmtCheckIDCheckDtCheckAmtCheckVoidDt
012008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundBERGER INC6741832008/03/19 03:59:59+002944.61770.03460142008/05/07 03:59:59+002944.61900/01/01 05:00:00+00
122008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundCED INC6890562008/05/07 03:59:59+00382.1382.13542662008/06/11 03:59:59+00282.11900/01/01 05:00:00+00
232008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundDINE COMPANY6890572008/06/03 03:59:59+00798.0798.03541952008/06/11 03:59:59+001136.31900/01/01 05:00:00+00
342008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundDIVERSIFIED SHEET METAL INC6890602008/05/15 03:59:59+001685.01685.03542842008/06/11 03:59:59+001685.01900/01/01 05:00:00+00
452008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundMARINE INDUSTRIES CORPORATION5767582007/04/10 03:59:59+00985.0985.02991112007/08/15 03:59:59+00985.01900/01/01 05:00:00+00
562008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundQUANTROL INC5768072007/07/16 03:59:59+003221.32225.03006142007/08/23 03:59:59+003221.31900/01/01 05:00:00+00
672008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundSTANLEY SCHULTZE & CO INC6741822008/04/14 03:59:59+00455.0455.03459282008/05/07 03:59:59+00455.01900/01/01 05:00:00+00
782008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundTECHNICAL SERVICE CORPORATION6890592008/05/05 03:59:59+002000.02000.03542862008/06/11 03:59:59+002000.01900/01/01 05:00:00+00
892008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundVITTITOW REFRIGERATION INC.6890332008/05/20 03:59:59+002925.02925.03550462008/06/16 03:59:59+002925.01900/01/01 05:00:00+00
9102008Metro Government CapitalWaterfront Development CorpBelle Capital ImprovementsNaNNaNContractual ServicesPayments to ContractorsNaNCapital Project FundW M LUMBER AND WOOD PRODUCTS INC6956362008/06/10 03:59:59+004690.03777.93576412008/06/30 03:59:59+004690.01900/01/01 05:00:00+00
ObjectIdFiscal_YearBudget_TypeAgency_NameSub_Agency_NameDepartmentNameSub_DepartmentNameCategorySub_CategoryStimulus_TypeFunding_SourceVendor_NameInvoiceIDInvoiceDtInvoiceAmtDistributionAmtCheckIDCheckDtCheckAmtCheckVoidDt
1296371296382008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYMCA OF GREATER LOUISVILLE INC6038292007/09/06 03:59:59+0013460.013460.03092082007/10/12 03:59:59+0013460.01900/01/01 05:00:00+00
1296381296392008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYMCA OF GREATER LOUISVILLE INC6491792008/02/01 05:00:00+0013700.013700.03327972008/02/14 05:00:00+0013700.01900/01/01 05:00:00+00
1296391296402008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUNG ADULT DEVELOPMENT IN ACTION INC6275902007/11/14 05:00:00+0014650.014650.03218982007/12/11 05:00:00+0014650.01900/01/01 05:00:00+00
1296401296412008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUNG ADULT DEVELOPMENT IN ACTION INC6511922008/02/14 05:00:00+0014650.014650.03339882008/02/21 05:00:00+0014650.01900/01/01 05:00:00+00
1296411296422008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUNG ADULT DEVELOPMENT IN ACTION INC5936882007/09/10 03:59:59+0014600.014600.03041512007/09/13 03:59:59+0014600.01900/01/01 05:00:00+00
1296421296432008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUNG ADULT DEVELOPMENT IN ACTION INC6751292008/04/28 03:59:59+0014650.014650.03455372008/05/05 03:59:59+0014650.01900/01/01 05:00:00+00
1296431296442008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUTH ALIVE INC5870062007/08/21 03:59:59+005875.05875.03012182007/08/27 03:59:59+005875.01900/01/01 05:00:00+00
1296441296452008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUTH ALIVE INC6228482007/10/19 03:59:59+005755.05755.03196852007/11/29 05:00:00+005755.01900/01/01 05:00:00+00
1296451296462008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUTH ALIVE INC6491772008/02/01 05:00:00+005875.05875.03328292008/02/14 05:00:00+005875.01900/01/01 05:00:00+00
1296461296472008Metro Government OperationsNeighborhoods DepartmentYouth DevelopmentYouth External FundNaNContractual ServicesExternal Agency Contractual ServicesNaNGeneral FundYOUTH ALIVE INC6793302008/05/12 03:59:59+005875.05875.03476442008/05/16 03:59:59+005875.01900/01/01 05:00:00+00